A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
This example setup for RouterOS 7.22 configures a MikroTik router as a PPPoE offloader. The router establishes a connection to a PPPoE server (e. g. over GPON or DSL), acquires an IPv4 address and an IPv6 prefix and delegates these to a downstream firewall via DHCP. The MikroTik device acts purely as a router, it has no firewall / NAT rules.
This is motivated by wanting a basic IP over Ethernet connection for the WAN interface of the main firewall (e. g. OPNsense). Specific hardware and protocols required for the WAN uplink are handled by the MikroTik device. RouterOS is known to have a robust PPPoE implementation.
It is assumed that the router has no default configuration like bridges, firewall rules etc.:
| // ==UserScript== | |
| // @name GLM Coding Rush - 智谱编程助手抢购脚本 | |
| // @namespace https://gist.github.com/LessUp | |
| // @version 1.1.0 | |
| // @description 智谱 GLM Coding 一键抢购脚本 — 自动解锁售罄按钮 / 高速重试引擎 / bizId 双重校验 / 错误弹窗自动恢复 / 支付弹窗保护 / 秒级定时触发 / 可拖拽浮动面板 | |
| // @author LessUp | |
| // @match *://www.bigmodel.cn/* | |
| // @match https://bigmodel.cn/glm-coding* | |
| // @run-at document-start | |
| // @grant none |
| Setting up xbps-src | |
| Docs | |
| 1. https://github.com/void-linux/void-packages/blob/master/Manual.md | |
| 1. https://github.com/void-linux/void-packages/blob/master/README.md | |
| $ clone https://github.com/void-linux/void-packages.git | |
| $ xbps-install xtools | |
| `xtools` provides `xi` (short for xbps-install). |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| {"version":1,"updated_at":"2026-04-29T09:02:46+08:00","codes":[{"code":"GE48-MKRR-93C3","device_id":"\/BQxlx\/RzzHNef0sKIvxEg==","type":1,"created_at":1777219692,"expire_at":1808904708,"status":1,"activated_at":0},{"code":"FXVT-QNAM-E5CV","device_id":"","type":1,"created_at":1777219692,"expire_at":0,"status":1,"activated_at":0},{"code":"JJQD-5C27-HZAH","device_id":"test-device-20260428173836","type":1,"created_at":1777219692,"expire_at":1808905116,"status":1,"activated_at":1777369116},{"code":"5UNM-FGFJ-J7LY","device_id":"nL4Zf9wpnt5J9te\/KO1ApQ==","type":1,"created_at":1777219692,"expire_at":1808905995,"status":1,"activated_at":0},{"code":"NEJN-MXR4-6DKZ","device_id":"Ri43tM4Q2sFjP\/u70RElQA==","type":1,"created_at":1777219692,"expire_at":1808911580,"status":1,"activated_at":0},{"code":"95XH-BLVN-LLFT","device_id":"","type":1,"created_at":1777222614,"expire_at":0,"status":0,"activated_at":0},{"code":"VKY3-Y5N4-G5HB","device_id":"","type":1,"created_at":1777222614,"expire_at":0,"status":0,"activated_at":0},{" |
This bot trades Polymarket's "BTC Up or Down" 5-minute binary markets. Every 5 minutes, Polymarket opens a market asking: "Will BTC be higher or lower than the opening price when this 5-minute window closes?" You buy "Up" or "Down" tokens at some price (e.g. $0.50–$0.95), and if you're right, each token pays out $1.00. If you're wrong, you lose your bet.
The bot uses technical analysis on real-time Binance BTC price data to predict the outcome, then places the trade on Polymarket right before the window closes — when we have the most information but (ideally) before the token price has fully priced in the outcome.
| # DSA Question Bank | |
| ## Arrays, Hashing, Prefix/Suffix | |
| 1. Find the second largest element in one pass without extra space. | |
| 2. Find the k largest distinct elements without fully sorting the array. | |
| 3. Find the two numbers that appear once when every other number appears twice. | |
| 4. Find the majority element that appears more than n / 2 times. | |
| 5. Find all elements that appear more than n / 3 times. | |
| 6. Count inversions in an array. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.